I've been analysing the Bosses in Deathless mode after I thought that some perks appeared more often than others. I mentioned this on threads here and in epic games forums. I'll be tabulating what perk and what element they have from DM 201 to DM 250. So far as I've thought, Immune to magic appears quite frequently, but not as frequently as I imagined as immune to super was almost just as frequent and I hardly noticed that most of the time, there wasn't even a perk!! There isn't an even distribution of the perks either. SO far from DM 201 to 213, I've tabulated the following:-

I've also noticed that each boss attack with the same element in a given play through. I hadn't noticed that before. There is a regular cycle to which element they attack with but so far that cycle has only been made up of 5 elements. I'm not sure if playing regular mode has an impact on this cycle as I haven't played regular mode in a long time, but I vaguely remember the other two members of Epic Games Forums who reached DM 185, all did so with a Worker that had Shock as his attack which means that the cycle exists for them and no DM bosses attack with ice, water or bright. How weird!

The reason I chose DM 264 was because there are 8 perks and 8 elements but it seem that only 5 elements are used. You might also notice that I highlighted DM 247 and DM 248. Based on my calculations of Ryth health when I reach that level, I expect the game to end. We will see what happens.

Ryth still seems to be on track to hit max health between DM 247 and DM 248. Immune Magic seems not to be as popular as I thought. Maybe it seemed that way because I hate it. There is no pattern to the perks neither does there seem to be an even distribution of them.

ManOfSteel/AvalonWizard has shared with me his data up to DM233 (he sent me an Email with the extra points).

Anyway, I think I finally have a functional (albeit a bit convoluted) predictor for a boss level based on the DM level.

Well,first some statistical analysis.I always have had the 'perception' that the boss scaling was somewhat a Geometrical/exponential rule, but, to tell the true, my early attempts to 'fit an exponential curve' with the data was always a hit/miss.

In the graph below, it's plotted the Worker level at DM(i) MINUS Worker level at DM(i-1)

In the graph below, i's plotted the Worker level at DM(i) DIVIDED BY Worker level at DM(i-1)

My first surprise was that, the geometric ratio was decreasing and tending to one (which means, it isn't really geometrical after all).second, at first, from the arithmetic series, one could infer that we have TWO series in it.In fact, those spikes were extremely periodical: DM5, DM10, DM15 and so on.

based on that, I separated the data set in two series, one with DM5,10,15... and the second with the rest.My Second surprise: with the remaining points, I observed the same behavior (now, with DM4,9,13 as spikes)...

Yes, You are reading it right, this is an order 20th polynomialwith the above predictor, I can 'trace' the difference between the real points and the predicted ones:Please, note the graph scale (10^4), this means the 'predictor' was wrong by as much as 12000+ in some cases !!!

OK, it's also possible to have a 2nd order predictor (albeit "weaker"), like:X=(level-96.0521739130435)/83.2434438152045Worker(X)=+1418482.86230611*(X^2) +3233989.50019826*(X^1) +1842888.7278009*(X^0)

which results inNote that the 'predictor' was wronger overall.

Now the real ones. I mean, separating the data in 5 series and having a 2nd oreder predictor for each series, the end result is:Worker Series :: level is 1+5*n X1=(level-96.625)/84.7799261002581 Worker(X1)=+1472096.3449248*(X1^2) +3315159.89305075*(X1^1) +1866649.33611373*(X1^0) Series :: level is 2+5*n X2=(level-98.875)/85.5266612385208 Worker(X2)=+1498161.66500911*(X2^2) +3420981.34180257*(X2^1) +1953178.40436627*(X2^0) Series :: level is 3+5*n X3=(level-96.4782608695652)/85.7776563220905 Worker(X3)=+1507004.43404048*(X3^2) +3344587.96329596*(X3^1) +1856015.32396128*(X3^0) Series :: level is 4+5*n X4=(level-90.5)/86.5128527110887 Worker(X4)=+1532997.73050088*(X4^2) +3159135.15671389*(X4^1) +1627902.15602417*(X4^0) Series :: level is 5+5*n X5=(level-96.875)/81.1575561056608 Worker(X5)=+1348911.5058102*(X5^2) +3183977.98782829*(X5^1) +1878959.80693189*(X5^0)

which results in

which is a far better predictor overall (most of predicted values are less than 100 points off the real values).

I will spare you all from the details (the actual polynomial coefficients),so below you will find a series of plots, each of them being the resulting "predictors err".

Obviously, the higher the polynomial order, the better the result, but the second order still delivers the better compromise.More objectively, just summing the errs in each of the above graph (for each predictor), it is easy to compare themAs one can see, a 11th order predictor is 'twice as good' as the second order, but at an incredible cost.

If someone would be interested in, I have used MatLab for the above analysis (this a standard math package for engineering).Octave should be compatible with it. anyway, the 'source code' is:

Based on those data points, it's possible both to predict the Worker's level based on the Play Through, and the Play Through based on Worker's level:Worker's Level = 129599/6480 * Play-Through^2 + 6184/103*Play-Through + 3553/24Play-Through = ( - 6184/103 + sqrt((6184/103)^2 - 4 * 129599/6480 * (3553/24 - Worker's)))/(2*129599/6480)

Hello people,well after quite a long time, and lots of back and forth, now I can say "how the worker's level progress", with certainty, both in Normal Game Mode (NG) and/or Deathless Mode (DM).

NGThe first worker is at level 150, thereafter, the next FIVE workers are the previous one plus 200.So, NG 1 : 150NG 2 : 350NG 3 : 550NG 4 : 750NG 5 : 950the next FIVE ones the diff. will be 400 (1350, 1750, 2150, 2550, 2950), the next FIVE ones the diff. will be 600, then 800, then 1000, and so on.That way it is possible to know the worker's level for a given play through, and at which play through a player is based on the worker's level.

Hmmmm. Interesting. This looks awesome. I'll devour this information in a minute. Thanks for the analysis THX!!!

Thanks,

By the way, take notice that those last three graphs are "diff/err ones"In each and everyone of them, what is plotted is the difference between the original/real value and the value given by the predictor.

Notice that those "single series predictor" the error was diverging,While with the 5 series approach, the error is bound to less than 100 (in general)

Hmmmm. Interesting. This looks awesome. I'll devour this information in a minute. Thanks for the analysis THX!!!

Thanks,

By the way, take notice that those last three graphs are "diff/err ones"In each and everyone of them, what is plotted is the difference between the original/real value and the value given by the predictor.

Notice that those "single series predictor" the error was diverging,While with the 5 series approach, the error is bound to less than 100 (in general)

I wonder if ChAIR actually has a formula or we are telling them stuff about their game that they don't even know. LOL. Maybe the game has an AI engine that is evolving on its own.

Hmmmm. Interesting. This looks awesome. I'll devour this information in a minute. Thanks for the analysis THX!!!

Thanks,

By the way, take notice that those last three graphs are "diff/err ones"In each and everyone of them, what is plotted is the difference between the original/real value and the value given by the predictor.

Notice that those "single series predictor" the error was diverging,While with the 5 series approach, the error is bound to less than 100 (in general)

I wonder if cHair actually has a formula or we are telling them stuff about their game that they don't even know. LOL. Maybe the game has an AI engine that is evolving on its own.

They certainly know it better,although, in the past, many have tried to 'get it straight from cHair', to no avail.

I'm going out to lunch now, but I should update the post above with a bit better, and more, graphs when I return

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